Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations327346
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.5 MiB
Average record size in memory152.0 B

Variable types

Categorical3
Numeric13
Text2

Alerts

year has constant value "2013" Constant
air_time is highly overall correlated with distanceHigh correlation
arr_delay is highly overall correlated with dep_delayHigh correlation
arr_time is highly overall correlated with dep_time and 3 other fieldsHigh correlation
carrier is highly overall correlated with originHigh correlation
dep_delay is highly overall correlated with arr_delayHigh correlation
dep_time is highly overall correlated with arr_time and 3 other fieldsHigh correlation
distance is highly overall correlated with air_timeHigh correlation
hour is highly overall correlated with arr_time and 3 other fieldsHigh correlation
origin is highly overall correlated with carrierHigh correlation
sched_arr_time is highly overall correlated with arr_time and 3 other fieldsHigh correlation
sched_dep_time is highly overall correlated with arr_time and 3 other fieldsHigh correlation
dep_delay has 16466 (5.0%) zeros Zeros
arr_delay has 5409 (1.7%) zeros Zeros
minute has 58924 (18.0%) zeros Zeros

Reproduction

Analysis started2025-02-17 10:50:10.446126
Analysis finished2025-02-17 10:50:28.159478
Duration17.71 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

year
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
2013
327346 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1309384
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2013 327346
100.0%

Length

2025-02-17T10:50:28.224940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T10:50:28.281898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2013 327346
100.0%

Most occurring characters

ValueCountFrequency (%)
2 327346
25.0%
0 327346
25.0%
1 327346
25.0%
3 327346
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1309384
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 327346
25.0%
0 327346
25.0%
1 327346
25.0%
3 327346
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1309384
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 327346
25.0%
0 327346
25.0%
1 327346
25.0%
3 327346
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1309384
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 327346
25.0%
0 327346
25.0%
1 327346
25.0%
3 327346
25.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.564803
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2025-02-17T10:50:28.328545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4134444
Coefficient of variation (CV)0.51996143
Kurtosis-1.1883285
Mean6.564803
Median Absolute Deviation (MAD)3
Skewness-0.023627093
Sum2148962
Variance11.651603
MonotonicityNot monotonic
2025-02-17T10:50:28.382717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 28756
8.8%
10 28618
8.7%
7 28293
8.6%
5 28128
8.6%
3 27902
8.5%
4 27564
8.4%
6 27075
8.3%
12 27020
8.3%
9 27010
8.3%
11 26971
8.2%
Other values (2) 50009
15.3%
ValueCountFrequency (%)
1 26398
8.1%
2 23611
7.2%
3 27902
8.5%
4 27564
8.4%
5 28128
8.6%
6 27075
8.3%
7 28293
8.6%
8 28756
8.8%
9 27010
8.3%
10 28618
8.7%
ValueCountFrequency (%)
12 27020
8.3%
11 26971
8.2%
10 28618
8.7%
9 27010
8.3%
8 28756
8.8%
7 28293
8.6%
6 27075
8.3%
5 28128
8.6%
4 27564
8.4%
3 27902
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.740825
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2025-02-17T10:50:28.439587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.777376
Coefficient of variation (CV)0.55761856
Kurtosis-1.1856003
Mean15.740825
Median Absolute Deviation (MAD)8
Skewness-0.0011255344
Sum5152696
Variance77.04233
MonotonicityNot monotonic
2025-02-17T10:50:28.501648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
15 11150
 
3.4%
18 11131
 
3.4%
3 11070
 
3.4%
21 11017
 
3.4%
22 10985
 
3.4%
11 10983
 
3.4%
20 10974
 
3.4%
17 10961
 
3.3%
4 10949
 
3.3%
27 10845
 
3.3%
Other values (21) 217281
66.4%
ValueCountFrequency (%)
1 10748
3.3%
2 10524
3.2%
3 11070
3.4%
4 10949
3.3%
5 10609
3.2%
6 10725
3.3%
7 10611
3.2%
8 10308
3.1%
9 10231
3.1%
10 10614
3.2%
ValueCountFrequency (%)
31 6038
1.8%
30 10023
3.1%
29 9916
3.0%
28 10394
3.2%
27 10845
3.3%
26 10724
3.3%
25 10827
3.3%
24 10741
3.3%
23 10511
3.2%
22 10985
3.4%

dep_time
Real number (ℝ)

High correlation 

Distinct1317
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1348.7899
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2025-02-17T10:50:28.570752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile624
Q1907
median1400
Q31744
95-th percentile2112
Maximum2400
Range2399
Interquartile range (IQR)837

Descriptive statistics

Standard deviation488.31998
Coefficient of variation (CV)0.36204303
Kurtosis-1.0890293
Mean1348.7899
Median Absolute Deviation (MAD)429
Skewness-0.023402925
Sum4.4152097 × 108
Variance238456.4
MonotonicityNot monotonic
2025-02-17T10:50:28.641655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 833
 
0.3%
556 817
 
0.2%
755 816
 
0.2%
557 798
 
0.2%
655 793
 
0.2%
1455 767
 
0.2%
1454 766
 
0.2%
654 745
 
0.2%
855 740
 
0.2%
756 739
 
0.2%
Other values (1307) 319532
97.6%
ValueCountFrequency (%)
1 25
< 0.1%
2 35
< 0.1%
3 26
< 0.1%
4 26
< 0.1%
5 20
< 0.1%
6 22
< 0.1%
7 22
< 0.1%
8 23
< 0.1%
9 28
< 0.1%
10 22
< 0.1%
ValueCountFrequency (%)
2400 29
 
< 0.1%
2359 54
< 0.1%
2358 76
< 0.1%
2357 74
< 0.1%
2356 74
< 0.1%
2355 82
< 0.1%
2354 69
< 0.1%
2353 68
< 0.1%
2352 68
< 0.1%
2351 57
< 0.1%

sched_dep_time
Real number (ℝ)

High correlation 

Distinct1020
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1340.3351
Minimum500
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2025-02-17T10:50:28.709240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile630
Q1905
median1355
Q31729
95-th percentile2050
Maximum2359
Range1859
Interquartile range (IQR)824

Descriptive statistics

Standard deviation467.41316
Coefficient of variation (CV)0.34872858
Kurtosis-1.1985202
Mean1340.3351
Median Absolute Deviation (MAD)412
Skewness0.0062355463
Sum4.3875333 × 108
Variance218475.06
MonotonicityNot monotonic
2025-02-17T10:50:28.783202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 6836
 
2.1%
700 4822
 
1.5%
630 4690
 
1.4%
900 4666
 
1.4%
1200 4521
 
1.4%
1700 4380
 
1.3%
1600 3971
 
1.2%
800 3862
 
1.2%
1300 3573
 
1.1%
1900 3544
 
1.1%
Other values (1010) 282481
86.3%
ValueCountFrequency (%)
500 340
0.1%
501 1
 
< 0.1%
505 2
 
< 0.1%
510 5
 
< 0.1%
515 205
0.1%
516 4
 
< 0.1%
517 27
 
< 0.1%
520 7
 
< 0.1%
525 37
 
< 0.1%
527 1
 
< 0.1%
ValueCountFrequency (%)
2359 810
0.2%
2358 44
 
< 0.1%
2355 73
 
< 0.1%
2352 16
 
< 0.1%
2345 1
 
< 0.1%
2339 1
 
< 0.1%
2330 14
 
< 0.1%
2315 1
 
< 0.1%
2305 61
 
< 0.1%
2300 21
 
< 0.1%

dep_delay
Real number (ℝ)

High correlation  Zeros 

Distinct526
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.555156
Minimum-43
Maximum1301
Zeros16466
Zeros (%)5.0%
Negative183135
Negative (%)55.9%
Memory size5.0 MiB
2025-02-17T10:50:28.855576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-43
5-th percentile-9
Q1-5
median-2
Q311
95-th percentile88
Maximum1301
Range1344
Interquartile range (IQR)16

Descriptive statistics

Standard deviation40.065688
Coefficient of variation (CV)3.1911741
Kurtosis44.355043
Mean12.555156
Median Absolute Deviation (MAD)4
Skewness4.8180179
Sum4109880
Variance1605.2593
MonotonicityNot monotonic
2025-02-17T10:50:28.929865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 24765
 
7.6%
-4 24557
 
7.5%
-3 24158
 
7.4%
-2 21463
 
6.6%
-6 20649
 
6.3%
-1 18761
 
5.7%
-7 16714
 
5.1%
0 16466
 
5.0%
-8 11770
 
3.6%
1 8026
 
2.5%
Other values (516) 140017
42.8%
ValueCountFrequency (%)
-43 1
 
< 0.1%
-33 1
 
< 0.1%
-32 1
 
< 0.1%
-30 1
 
< 0.1%
-27 1
 
< 0.1%
-26 1
 
< 0.1%
-25 2
 
< 0.1%
-24 4
 
< 0.1%
-23 6
< 0.1%
-22 11
< 0.1%
ValueCountFrequency (%)
1301 1
< 0.1%
1137 1
< 0.1%
1126 1
< 0.1%
1014 1
< 0.1%
1005 1
< 0.1%
960 1
< 0.1%
911 1
< 0.1%
899 1
< 0.1%
898 1
< 0.1%
896 1
< 0.1%

arr_time
Real number (ℝ)

High correlation 

Distinct1410
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1501.9082
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2025-02-17T10:50:29.002596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile736
Q11104
median1535
Q31940
95-th percentile2248
Maximum2400
Range2399
Interquartile range (IQR)836

Descriptive statistics

Standard deviation532.88873
Coefficient of variation (CV)0.35480778
Kurtosis-0.19467805
Mean1501.9082
Median Absolute Deviation (MAD)418
Skewness-0.46569259
Sum4.9164365 × 108
Variance283970.4
MonotonicityNot monotonic
2025-02-17T10:50:29.200904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1008 484
 
0.1%
1013 484
 
0.1%
1015 479
 
0.1%
1012 464
 
0.1%
1005 460
 
0.1%
1006 459
 
0.1%
1016 459
 
0.1%
1011 457
 
0.1%
1007 456
 
0.1%
1040 455
 
0.1%
Other values (1400) 322689
98.6%
ValueCountFrequency (%)
1 201
0.1%
2 163
< 0.1%
3 174
0.1%
4 172
0.1%
5 205
0.1%
6 148
< 0.1%
7 169
0.1%
8 147
< 0.1%
9 140
< 0.1%
10 176
0.1%
ValueCountFrequency (%)
2400 150
< 0.1%
2359 221
0.1%
2358 187
0.1%
2357 207
0.1%
2356 201
0.1%
2355 205
0.1%
2354 195
0.1%
2353 181
0.1%
2352 191
0.1%
2351 214
0.1%

sched_arr_time
Real number (ℝ)

High correlation 

Distinct1162
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1532.7884
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2025-02-17T10:50:29.274622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile815
Q11122
median1554
Q31944
95-th percentile2246
Maximum2359
Range2358
Interquartile range (IQR)822

Descriptive statistics

Standard deviation497.97912
Coefficient of variation (CV)0.32488445
Kurtosis-0.3845489
Mean1532.7884
Median Absolute Deviation (MAD)416
Skewness-0.34447865
Sum5.0175216 × 108
Variance247983.21
MonotonicityNot monotonic
2025-02-17T10:50:29.346229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025 1294
 
0.4%
2015 1201
 
0.4%
1110 1191
 
0.4%
1115 1163
 
0.4%
1235 1119
 
0.3%
2359 1091
 
0.3%
1815 1064
 
0.3%
1015 1057
 
0.3%
1220 1056
 
0.3%
1645 1047
 
0.3%
Other values (1152) 316063
96.6%
ValueCountFrequency (%)
1 235
0.1%
2 92
 
< 0.1%
3 158
< 0.1%
4 103
< 0.1%
5 82
 
< 0.1%
6 18
 
< 0.1%
7 84
 
< 0.1%
8 151
< 0.1%
9 54
 
< 0.1%
10 72
 
< 0.1%
ValueCountFrequency (%)
2359 1091
0.3%
2358 481
0.1%
2357 345
 
0.1%
2356 460
0.1%
2355 329
 
0.1%
2354 381
 
0.1%
2353 257
 
0.1%
2352 44
 
< 0.1%
2351 140
 
< 0.1%
2350 105
 
< 0.1%

arr_delay
Real number (ℝ)

High correlation  Zeros 

Distinct577
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8953768
Minimum-86
Maximum1272
Zeros5409
Zeros (%)1.7%
Negative188933
Negative (%)57.7%
Memory size5.0 MiB
2025-02-17T10:50:29.416588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-86
5-th percentile-32
Q1-17
median-5
Q314
95-th percentile91
Maximum1272
Range1358
Interquartile range (IQR)31

Descriptive statistics

Standard deviation44.633292
Coefficient of variation (CV)6.4729301
Kurtosis29.233044
Mean6.8953768
Median Absolute Deviation (MAD)14
Skewness3.7168175
Sum2257174
Variance1992.1307
MonotonicityNot monotonic
2025-02-17T10:50:29.492871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-13 7177
 
2.2%
-10 7088
 
2.2%
-12 7046
 
2.2%
-14 6975
 
2.1%
-11 6863
 
2.1%
-9 6815
 
2.1%
-15 6796
 
2.1%
-7 6677
 
2.0%
-17 6668
 
2.0%
-8 6663
 
2.0%
Other values (567) 258578
79.0%
ValueCountFrequency (%)
-86 1
 
< 0.1%
-79 1
 
< 0.1%
-75 2
 
< 0.1%
-74 1
 
< 0.1%
-73 1
 
< 0.1%
-71 3
 
< 0.1%
-70 8
< 0.1%
-69 7
< 0.1%
-68 12
< 0.1%
-67 7
< 0.1%
ValueCountFrequency (%)
1272 1
< 0.1%
1127 1
< 0.1%
1109 1
< 0.1%
1007 1
< 0.1%
989 1
< 0.1%
931 1
< 0.1%
915 1
< 0.1%
895 1
< 0.1%
878 1
< 0.1%
875 1
< 0.1%

carrier
Categorical

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
UA
57782 
B6
54049 
EV
51108 
DL
47658 
AA
31947 
Other values (11)
84802 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters654692
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUA
2nd rowUA
3rd rowAA
4th rowB6
5th rowDL

Common Values

ValueCountFrequency (%)
UA 57782
17.7%
B6 54049
16.5%
EV 51108
15.6%
DL 47658
14.6%
AA 31947
9.8%
MQ 25037
7.6%
US 19831
 
6.1%
9E 17294
 
5.3%
WN 12044
 
3.7%
VX 5116
 
1.6%
Other values (6) 5480
 
1.7%

Length

2025-02-17T10:50:29.561749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ua 57782
17.7%
b6 54049
16.5%
ev 51108
15.6%
dl 47658
14.6%
aa 31947
9.8%
mq 25037
7.6%
us 19831
 
6.1%
9e 17294
 
5.3%
wn 12044
 
3.7%
vx 5116
 
1.6%
Other values (6) 5480
 
1.7%

Most occurring characters

ValueCountFrequency (%)
A 122727
18.7%
U 77613
11.9%
E 68402
10.4%
V 56768
8.7%
B 54049
8.3%
6 54049
8.3%
L 50833
7.8%
D 47658
 
7.3%
Q 25037
 
3.8%
M 25037
 
3.8%
Other values (9) 72519
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 654692
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 122727
18.7%
U 77613
11.9%
E 68402
10.4%
V 56768
8.7%
B 54049
8.3%
6 54049
8.3%
L 50833
7.8%
D 47658
 
7.3%
Q 25037
 
3.8%
M 25037
 
3.8%
Other values (9) 72519
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 654692
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 122727
18.7%
U 77613
11.9%
E 68402
10.4%
V 56768
8.7%
B 54049
8.3%
6 54049
8.3%
L 50833
7.8%
D 47658
 
7.3%
Q 25037
 
3.8%
M 25037
 
3.8%
Other values (9) 72519
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 654692
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 122727
18.7%
U 77613
11.9%
E 68402
10.4%
V 56768
8.7%
B 54049
8.3%
6 54049
8.3%
L 50833
7.8%
D 47658
 
7.3%
Q 25037
 
3.8%
M 25037
 
3.8%
Other values (9) 72519
11.1%

flight
Real number (ℝ)

Distinct3835
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1943.1045
Minimum1
Maximum8500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2025-02-17T10:50:29.625428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile87
Q1544
median1467
Q33412
95-th percentile4689
Maximum8500
Range8499
Interquartile range (IQR)2868

Descriptive statistics

Standard deviation1621.5237
Coefficient of variation (CV)0.83450153
Kurtosis-0.79075907
Mean1943.1045
Median Absolute Deviation (MAD)1049
Skewness0.69309682
Sum6.3606749 × 108
Variance2629339.1
MonotonicityNot monotonic
2025-02-17T10:50:29.693367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 956
 
0.3%
27 886
 
0.3%
181 875
 
0.3%
301 852
 
0.3%
161 780
 
0.2%
695 756
 
0.2%
1109 709
 
0.2%
745 697
 
0.2%
1 697
 
0.2%
359 694
 
0.2%
Other values (3825) 319444
97.6%
ValueCountFrequency (%)
1 697
0.2%
2 51
 
< 0.1%
3 628
0.2%
4 391
0.1%
5 324
0.1%
6 206
 
0.1%
7 236
 
0.1%
8 234
 
0.1%
9 152
 
< 0.1%
10 61
 
< 0.1%
ValueCountFrequency (%)
8500 1
 
< 0.1%
6181 80
< 0.1%
6180 6
 
< 0.1%
6177 160
< 0.1%
6168 2
 
< 0.1%
6167 3
 
< 0.1%
6165 1
 
< 0.1%
6140 1
 
< 0.1%
6138 2
 
< 0.1%
6120 29
 
< 0.1%
Distinct4037
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
2025-02-17T10:50:29.868730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9951794
Min length5

Characters and Unicode

Total characters1962498
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)0.1%

Sample

1st rowN14228
2nd rowN24211
3rd rowN619AA
4th rowN804JB
5th rowN668DN
ValueCountFrequency (%)
n725mq 544
 
0.2%
n722mq 485
 
0.1%
n723mq 475
 
0.1%
n711mq 462
 
0.1%
n713mq 449
 
0.1%
n258jb 420
 
0.1%
n353jb 403
 
0.1%
n298jb 402
 
0.1%
n351jb 391
 
0.1%
n328aa 389
 
0.1%
Other values (4027) 322926
98.6%
2025-02-17T10:50:30.117349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 353593
18.0%
3 146776
 
7.5%
1 140633
 
7.2%
5 132455
 
6.7%
A 116735
 
5.9%
7 112442
 
5.7%
2 107781
 
5.5%
9 103977
 
5.3%
4 100116
 
5.1%
6 99412
 
5.1%
Other values (24) 548578
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1962498
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 353593
18.0%
3 146776
 
7.5%
1 140633
 
7.2%
5 132455
 
6.7%
A 116735
 
5.9%
7 112442
 
5.7%
2 107781
 
5.5%
9 103977
 
5.3%
4 100116
 
5.1%
6 99412
 
5.1%
Other values (24) 548578
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1962498
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 353593
18.0%
3 146776
 
7.5%
1 140633
 
7.2%
5 132455
 
6.7%
A 116735
 
5.9%
7 112442
 
5.7%
2 107781
 
5.5%
9 103977
 
5.3%
4 100116
 
5.1%
6 99412
 
5.1%
Other values (24) 548578
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1962498
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 353593
18.0%
3 146776
 
7.5%
1 140633
 
7.2%
5 132455
 
6.7%
A 116735
 
5.9%
7 112442
 
5.7%
2 107781
 
5.5%
9 103977
 
5.3%
4 100116
 
5.1%
6 99412
 
5.1%
Other values (24) 548578
28.0%

origin
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
EWR
117127 
JFK
109079 
LGA
101140 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters982038
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEWR
2nd rowLGA
3rd rowJFK
4th rowJFK
5th rowLGA

Common Values

ValueCountFrequency (%)
EWR 117127
35.8%
JFK 109079
33.3%
LGA 101140
30.9%

Length

2025-02-17T10:50:30.198240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T10:50:30.249239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ewr 117127
35.8%
jfk 109079
33.3%
lga 101140
30.9%

Most occurring characters

ValueCountFrequency (%)
E 117127
11.9%
W 117127
11.9%
R 117127
11.9%
J 109079
11.1%
F 109079
11.1%
K 109079
11.1%
L 101140
10.3%
G 101140
10.3%
A 101140
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 982038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 117127
11.9%
W 117127
11.9%
R 117127
11.9%
J 109079
11.1%
F 109079
11.1%
K 109079
11.1%
L 101140
10.3%
G 101140
10.3%
A 101140
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 982038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 117127
11.9%
W 117127
11.9%
R 117127
11.9%
J 109079
11.1%
F 109079
11.1%
K 109079
11.1%
L 101140
10.3%
G 101140
10.3%
A 101140
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 982038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 117127
11.9%
W 117127
11.9%
R 117127
11.9%
J 109079
11.1%
F 109079
11.1%
K 109079
11.1%
L 101140
10.3%
G 101140
10.3%
A 101140
10.3%

dest
Text

Distinct104
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
2025-02-17T10:50:30.369112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters982038
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowIAH
2nd rowIAH
3rd rowMIA
4th rowBQN
5th rowATL
ValueCountFrequency (%)
atl 16837
 
5.1%
ord 16566
 
5.1%
lax 16026
 
4.9%
bos 15022
 
4.6%
mco 13967
 
4.3%
clt 13674
 
4.2%
sfo 13173
 
4.0%
fll 11897
 
3.6%
mia 11593
 
3.5%
dca 9111
 
2.8%
Other values (94) 189480
57.9%
2025-02-17T10:50:30.564037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 105000
 
10.7%
L 91666
 
9.3%
S 84108
 
8.6%
D 74576
 
7.6%
O 67582
 
6.9%
C 62155
 
6.3%
T 59938
 
6.1%
M 57577
 
5.9%
I 41300
 
4.2%
F 40848
 
4.2%
Other values (16) 297288
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 982038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 105000
 
10.7%
L 91666
 
9.3%
S 84108
 
8.6%
D 74576
 
7.6%
O 67582
 
6.9%
C 62155
 
6.3%
T 59938
 
6.1%
M 57577
 
5.9%
I 41300
 
4.2%
F 40848
 
4.2%
Other values (16) 297288
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 982038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 105000
 
10.7%
L 91666
 
9.3%
S 84108
 
8.6%
D 74576
 
7.6%
O 67582
 
6.9%
C 62155
 
6.3%
T 59938
 
6.1%
M 57577
 
5.9%
I 41300
 
4.2%
F 40848
 
4.2%
Other values (16) 297288
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 982038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 105000
 
10.7%
L 91666
 
9.3%
S 84108
 
8.6%
D 74576
 
7.6%
O 67582
 
6.9%
C 62155
 
6.3%
T 59938
 
6.1%
M 57577
 
5.9%
I 41300
 
4.2%
F 40848
 
4.2%
Other values (16) 297288
30.3%

air_time
Real number (ℝ)

High correlation 

Distinct509
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.68646
Minimum20
Maximum695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2025-02-17T10:50:30.644732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q182
median129
Q3192
95-th percentile339
Maximum695
Range675
Interquartile range (IQR)110

Descriptive statistics

Standard deviation93.688305
Coefficient of variation (CV)0.62174335
Kurtosis0.86307699
Mean150.68646
Median Absolute Deviation (MAD)51
Skewness1.0707052
Sum49326610
Variance8777.4984
MonotonicityNot monotonic
2025-02-17T10:50:30.719883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 2552
 
0.8%
43 2543
 
0.8%
41 2513
 
0.8%
45 2495
 
0.8%
40 2466
 
0.8%
44 2444
 
0.7%
39 2411
 
0.7%
47 2409
 
0.7%
46 2406
 
0.7%
109 2377
 
0.7%
Other values (499) 302730
92.5%
ValueCountFrequency (%)
20 2
 
< 0.1%
21 14
 
< 0.1%
22 34
 
< 0.1%
23 82
 
< 0.1%
24 103
< 0.1%
25 124
< 0.1%
26 169
0.1%
27 147
< 0.1%
28 180
0.1%
29 209
0.1%
ValueCountFrequency (%)
695 1
< 0.1%
691 1
< 0.1%
686 2
< 0.1%
683 1
< 0.1%
679 1
< 0.1%
676 2
< 0.1%
675 1
< 0.1%
671 2
< 0.1%
669 1
< 0.1%
667 2
< 0.1%

distance
Real number (ℝ)

High correlation 

Distinct213
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1048.3713
Minimum80
Maximum4983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2025-02-17T10:50:30.795212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile199
Q1509
median888
Q31389
95-th percentile2475
Maximum4983
Range4903
Interquartile range (IQR)880

Descriptive statistics

Standard deviation735.90852
Coefficient of variation (CV)0.70195408
Kurtosis1.1491185
Mean1048.3713
Median Absolute Deviation (MAD)400
Skewness1.1133926
Sum3.4318016 × 108
Variance541561.35
MonotonicityNot monotonic
2025-02-17T10:50:30.868459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2475 11159
 
3.4%
762 10041
 
3.1%
733 8507
 
2.6%
2586 8109
 
2.5%
544 5961
 
1.8%
719 5828
 
1.8%
187 5773
 
1.8%
1096 5702
 
1.7%
2454 5646
 
1.7%
944 5429
 
1.7%
Other values (203) 255191
78.0%
ValueCountFrequency (%)
80 48
 
< 0.1%
94 895
 
0.3%
96 598
 
0.2%
116 412
 
0.1%
143 418
 
0.1%
160 358
 
0.1%
169 524
 
0.2%
173 210
 
0.1%
184 5150
1.6%
185 15
 
< 0.1%
ValueCountFrequency (%)
4983 342
 
0.1%
4963 359
 
0.1%
3370 8
 
< 0.1%
2586 8109
2.5%
2576 309
 
0.1%
2569 328
 
0.1%
2565 5064
1.5%
2521 282
 
0.1%
2475 11159
3.4%
2465 1031
 
0.3%

hour
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.14101
Minimum5
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2025-02-17T10:50:30.927347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median13
Q317
95-th percentile20
Maximum23
Range18
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6620629
Coefficient of variation (CV)0.35477204
Kurtosis-1.206908
Mean13.14101
Median Absolute Deviation (MAD)4
Skewness0.01154288
Sum4301657
Variance21.734831
MonotonicityNot monotonic
2025-02-17T10:50:30.988142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
8 26734
 
8.2%
6 25447
 
7.8%
17 23667
 
7.2%
15 23082
 
7.1%
7 22475
 
6.9%
16 22045
 
6.7%
18 21072
 
6.4%
14 21022
 
6.4%
19 20507
 
6.3%
9 19931
 
6.1%
Other values (9) 101364
31.0%
ValueCountFrequency (%)
5 1940
 
0.6%
6 25447
7.8%
7 22475
6.9%
8 26734
8.2%
9 19931
6.1%
10 16370
5.0%
11 15689
4.8%
12 17744
5.4%
13 19457
5.9%
14 21022
6.4%
ValueCountFrequency (%)
23 1042
 
0.3%
22 2558
 
0.8%
21 10503
3.2%
20 16061
4.9%
19 20507
6.3%
18 21072
6.4%
17 23667
7.2%
16 22045
6.7%
15 23082
7.1%
14 21022
6.4%

minute
Real number (ℝ)

Zeros 

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.234116
Minimum0
Maximum59
Zeros58924
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size5.0 MiB
2025-02-17T10:50:31.056029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median29
Q344
95-th percentile58
Maximum59
Range59
Interquartile range (IQR)36

Descriptive statistics

Standard deviation19.295918
Coefficient of variation (CV)0.73552765
Kurtosis-1.2345875
Mean26.234116
Median Absolute Deviation (MAD)16
Skewness0.092571479
Sum8587633
Variance372.33244
MonotonicityNot monotonic
2025-02-17T10:50:31.127794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58924
18.0%
30 33033
 
10.1%
45 19871
 
6.1%
15 18365
 
5.6%
55 18290
 
5.6%
59 15817
 
4.8%
10 14135
 
4.3%
25 14030
 
4.3%
5 13690
 
4.2%
29 13453
 
4.1%
Other values (50) 107738
32.9%
ValueCountFrequency (%)
0 58924
18.0%
1 2085
 
0.6%
2 818
 
0.2%
3 1381
 
0.4%
4 1322
 
0.4%
5 13690
 
4.2%
6 1343
 
0.4%
7 1067
 
0.3%
8 1645
 
0.5%
9 1403
 
0.4%
ValueCountFrequency (%)
59 15817
4.8%
58 1038
 
0.3%
57 1335
 
0.4%
56 1665
 
0.5%
55 18290
5.6%
54 1362
 
0.4%
53 1354
 
0.4%
52 1251
 
0.4%
51 1155
 
0.4%
50 12160
3.7%

Interactions

2025-02-17T10:50:26.404310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:15.673865image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.645710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.507360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.397753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:19.272789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.201008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.064141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.904556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.835312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.672164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.576344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:25.534636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.467743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:15.769349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.710132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.572029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.463074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:19.337150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.265973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.126909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.970818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.898267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.738568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.640520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:25.599159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.533224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:15.859131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.775093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.636374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.533677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:19.402215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.332133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.191936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.035981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.963030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.807050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.708495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:25.666099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.597369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:15.938229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.838588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.698410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.597849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:19.468197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.394969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.258989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.100001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.025533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.874305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.775409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:25.732228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.666142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.009046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.907528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.763935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.667132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:19.537767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.463974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.326867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.171826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.093298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.945212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.844484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:25.801621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.733144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.074890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.974410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.829158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.733985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:19.605635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.528557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.392353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.236722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.158018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.012613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.912117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:25.868335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.796872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.142072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.039040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.890630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.800058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:19.670642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.591664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.455596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.380426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.221199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.079274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.975715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:25.934432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.858760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.205441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.100899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.953957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.864389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:19.732714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.651559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.515482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.443809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.282452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.145274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:25.040264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.000760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.923715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.269006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.166087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.075920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.932514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:19.798372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.717226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.579743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.509487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.347147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.218169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:25.107423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.068947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.984693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.329350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.225586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.138105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.996615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:19.929920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.777801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.640684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.571010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.406576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.282585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:25.172972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.132974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:27.055245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.396003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.291160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.203600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:19.069344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.000119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.845299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.707712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.638086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.473697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.367911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:25.240803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.202309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:27.127254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.515277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.357096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.265173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:19.135493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.064213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.926548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.771791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.700312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.539289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.435802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:25.401836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.266385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:27.196597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:16.581590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:17.443423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:18.332056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:19.205943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.133098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:20.997540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:21.839729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:22.767618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:23.607541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:24.509258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:25.471378image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-02-17T10:50:26.333790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-02-17T10:50:31.184958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
air_timearr_delayarr_timecarrierdaydep_delaydep_timedistanceflighthourminutemonthoriginsched_arr_timesched_dep_time
air_time1.000-0.0230.0570.3580.0030.079-0.0300.984-0.479-0.0320.0340.0050.2450.077-0.029
arr_delay-0.0231.0000.1200.040-0.0000.6260.206-0.0740.0680.1560.023-0.0150.0230.1230.157
arr_time0.0570.1201.0000.106-0.0040.1920.8040.0530.0100.7850.056-0.0040.1160.8720.787
carrier0.3580.0400.1061.0000.0000.0330.1070.4020.4570.1290.1050.0140.5910.1360.133
day0.003-0.000-0.0040.0001.0000.006-0.0000.0040.000-0.0000.0010.0050.000-0.002-0.000
dep_delay0.0790.6260.1920.0330.0061.0000.2890.077-0.0280.2290.062-0.0160.0190.2170.232
dep_time-0.0300.2060.8040.107-0.0000.2891.000-0.0290.0340.9700.091-0.0040.1090.8770.972
distance0.984-0.0740.0530.4020.0040.077-0.0291.000-0.481-0.0300.0340.0180.2580.078-0.027
flight-0.4790.0680.0100.4570.000-0.0280.034-0.4811.0000.0250.0030.0070.319-0.0050.025
hour-0.0320.1560.7850.129-0.0000.2290.970-0.0300.0251.0000.034-0.0040.1310.8790.998
minute0.0340.0230.0560.1050.0010.0620.0910.0340.0030.0341.0000.0140.1230.0620.095
month0.005-0.015-0.0040.0140.005-0.016-0.0040.0180.007-0.0040.0141.0000.021-0.004-0.003
origin0.2450.0230.1160.5910.0000.0190.1090.2580.3190.1310.1230.0211.0000.1370.140
sched_arr_time0.0770.1230.8720.136-0.0020.2170.8770.078-0.0050.8790.062-0.0040.1371.0000.881
sched_dep_time-0.0290.1570.7870.133-0.0000.2320.972-0.0270.0250.9980.095-0.0030.1400.8811.000

Missing values

2025-02-17T10:50:27.302807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-17T10:50:27.597797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

yearmonthdaydep_timesched_dep_timedep_delayarr_timesched_arr_timearr_delaycarrierflighttailnumorigindestair_timedistancehourminute
0201311517.05152.0830.081911.0UA1545N14228EWRIAH227.01400515
1201311533.05294.0850.083020.0UA1714N24211LGAIAH227.01416529
2201311542.05402.0923.085033.0AA1141N619AAJFKMIA160.01089540
3201311544.0545-1.01004.01022-18.0B6725N804JBJFKBQN183.01576545
4201311554.0600-6.0812.0837-25.0DL461N668DNLGAATL116.076260
5201311554.0558-4.0740.072812.0UA1696N39463EWRORD150.0719558
6201311555.0600-5.0913.085419.0B6507N516JBEWRFLL158.0106560
7201311557.0600-3.0709.0723-14.0EV5708N829ASLGAIAD53.022960
8201311557.0600-3.0838.0846-8.0B679N593JBJFKMCO140.094460
9201311558.0600-2.0753.07458.0AA301N3ALAALGAORD138.073360
yearmonthdaydep_timesched_dep_timedep_delayarr_timesched_arr_timearr_delaycarrierflighttailnumorigindestair_timedistancehourminute
33676020139302211.0205972.02339.0224257.0EV4672N12145EWRSTL120.08722059
33676120139302231.02245-14.02335.02356-21.0B6108N193JBJFKPWM48.02732245
33676220139302233.0211380.0112.03042.0UA471N578UAEWRSFO318.025652113
33676320139302235.02001154.059.02249130.0B61083N804JBJFKMCO123.0944201
33676420139302237.02245-8.02345.02353-8.0B6234N318JBJFKBTV43.02662245
33676520139302240.02245-5.02334.02351-17.0B61816N354JBJFKSYR41.02092245
33676620139302240.02250-10.02347.07-20.0B62002N281JBJFKBUF52.03012250
33676720139302241.02246-5.02345.01-16.0B6486N346JBJFKROC47.02642246
33676820139302307.0225512.02359.023581.0B6718N565JBJFKBOS33.01872255
33676920139302349.02359-10.0325.0350-25.0B6745N516JBJFKPSE196.016172359